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Reductions of 3:1 were applied at 300 and 350ºC up to six passes (6p).
Table 1 shows applied force (Fmax, in kN), (sub)grain data (from TEM) and Hardness Vickers HV for a set of ECAPed samples.
Regarding ARB samples, Fig. 1 shows (sub)grain data from TEM observations with a 3:1 thickness reduction at processing temperatures of 300 and 350˚C.
At 400ºC both ef and YS data show that GBS is no longer operating and a change to a slip creep mechanism takes place.
This is clear when different processing temperatures are taken into account, as it is represented in Fig. 4b, for the same ECAP data of Fig. 4a, for which no (sub)grain size can be predicted from total applied strain data.

Due to its size the segregation coefficient can be derived from in-situ data only for very thin Aulayers (dAu � 1nm at a sample thickness of � 1600µm).
The data can be described quite nicely by diffusion of Coi and a segregation coefficient as given by Eq. 1.
Fitting the data gives a diffusion coefficient comparable with that from the literature [7].
Currently available data still shows strong inverse gettering at 870� C (measured in mc-Si), while it is already quite weak at 930� C.
Since the behaviour is quite similar to that described for CZ-Si above, the data is not shown.

Combining PALS data with that obtained by the coincidence Doppler broadening technique (described below) enables distinction between these components.
Contingency table analysis of the APT data was used to assess the degree of interaction between Cu and Mg solute atoms.
Note that the data presented here was not smoothed or symmetrised.
We have performed a contingency table analysis (CTA) on a systematic series of AP data sets following quenching, ageing for 60 sec and for 1 h at 150 °C.
This preferred interaction was also observed in data from samples aged 60 sec and 1 h at 150 °C.

The "threesome" comprising availability, ability to evaluate and accuracy of data will continue in the future to retain its strong significance.
Furthermore, competence in making data from various sources and in various forms available on a common basis and interlinking these data in a suitable manner will become increasingly important.
· Operation is simple and the transparency is high because the respective data required for the various user groups can be provided in a targeted manner (data compression)
This system makes it possible to record, compress, evaluate and store measured data.
The calculated data are shown with the green triangles and are in accordance with the measured data (red squares).This makes it possible for the mill owner to produce higher-strength materials or to reduce the content of alloying elements if the strength is to remain unchanged.

With comparing the datasets of VQ/DTW, VQ/DHMM, CHMM state-1 recognition algorithm and CHMM state-2 recognition algorithm, the results of our experiment show that: (1) CHMM state-2 branch method performs primely in reduction of the recognition time; and (2) the recognition accuracy is improved eventually.
Meanwhile, another feature is to extract a characteristic sequence from each phonetic frame data, including Linear prediction coefficients of signals, Linear Predictive Cepstrum Coefficients(LPCC) and Mel Frequency Cepstrum Coefficient(MFCC).
Conclusions and Future Work We propose some improvement strategy on account to the problems mentioned above: (1) Restrict the data sample accuracy; (2) Marking manually could reduce the accuracy of classification as well as the capability of self-learning, while program is implemented simply as well as in favor of training

The variation of voltage signal across a resistance connected in series with sensor was monitored and recorded with a computer controlled data handling system (Fig. 4b).
Fig. 4(a) Schematics of gas sensor testing chamber Fig. 4(b) Schematics of data acquisition system 6.3 Structural Analysis.
Plots of X-ray diffraction data of tin dioxide samples prepared at 5oC and sintered at 400, 600 and 800oC, respectively, are shown in figure 5.
At low temperature (5 oC in present study) a higher supersaturation leads to large reduction in Gibbs free energy.
This energy reduction appears as an increased surface energy favouring continued nucleation with smaller sizes.

Three scenarios (present, 10 years, and 10 years with 25% BOD reduction) were simulated.
The seasonal effect on Lam Takong water quality: Data of rain fall [6] and water quality of Lam Takong Basin in 2008-2009 were used for compare correlation water qualities between dry (October, December 2008 and February 2009) and rainy season.
Land use type classification: Land use data file of Nakhon Ratchasima province in 2008 [7] and Topographic map (Scale 1: 50,000) were classified land use type in Lam Takong Basin.
Three scenarios (present, 10 years, and 10 years with 25% BOD reduction) were simulated (Figure 4).
We would like to thank the officers of The Pollution Control Department, Regional Environmental Office 11, Nakhon Ratchasima Municipality, the Royal Irrigation Department, and the Thai Meteorology Department for providing basic data.

In order to solve fish classification problems based on acoustic scattering data, temporal centroid(TC) features and discrete cosine transform(DCT) coefficients features used to analyze acoustic scattering characteristics of fish from different aspects are extracted.
The processing results of actual experimental data show that multi-feature fusion method can improve the identification rate at about 5% effectively.
Features extraction for fish Acquisition method of acoustic scaterring data.
Features extraction process is shown in Fig.3, which includes features extraction, features fusion, features dimension reduction, etc.
The data acquisition method are shown in Fig.2, a total of 500 groups of sample data, wherein the 200 groups are the classifier training samples sets, the remaining 300 groups are the classifier test samples sets, training samples sets and test samples sets are constructed through the way of random extraction.

Data were acquired taking into account an expected life depending on the cutting conditions.
These signals were processed and logged via a data acquisition card connected directly to a PC.
The same type of network was used to develop a TCMS with the experiment data.
The key issue in many monitoring systems is the reduction of large flows of data from numerous sensors to a few well-correlated features that can be used for process monitoring.
Of the 42 observations given in table 2, the data that are presented in normal type were used to train the network (30 observations) and the data in italics type for validation (12 observations).

To acquire Small Angle Scattering data, the samples were measured in a SAXS NANOSTAR by Bruker AXS Inc..
The reduction of the 2d-data to a 1d-scattering curve was performed with the IGOR Pro macro NIKA [13].
However, an atomistic interpretation of these data is not possible at this stage.
This is also clearly seen from the DSC data taken from the weld nugget showing the formation of T1.
Ilavsky, NIKA: software for two-dimensional data reduction, J.